main.py

from fastapi import FastAPI, HTTPException, Header, Depends
from pydantic import BaseModel
import requests
import os
import json
from typing import Optional, Dict

app = FastAPI(title="CygnisAI Studio API")

# --- CONFIGURATION ---
# Token HF pour appeler les modèles (à configurer dans les Secrets du Space)
HF_TOKEN = os.environ.get("HF_TOKEN")

# Clé API statique pour sécuriser VOTRE API (à configurer dans les Secrets du Space)
# Par défaut pour le test local :
CYGNIS_API_KEY = os.environ.get("CYGNIS_API_KEY", "cgn_live_stable_demo_api_key_012345")

# Mapping des modèles demandés vers les endpoints réels Hugging Face
# Note: J'ai mappé vers les modèles réels les plus proches car Llama 4 / Gemma 3 n'existent pas encore publiquement.
# Vous pourrez mettre à jour ces IDs dès leur sortie.
MODELS = {
    "google/gemma-3-27b-it": "google/gemma-2-27b-it", # Fallback Gemma 2
    "openai/gpt-oss-120b": "meta-llama/Meta-Llama-3.1-70B-Instruct", # Fallback Llama 3.1 70B (puissant)
    "Qwen/Qwen3-VL-8B-Thinking": "Qwen/Qwen2-VL-7B-Instruct", # Fallback Qwen 2 VL
    "XiaomiMiMo/MiMo-V2-Flash": "Xiaomi/MIMO", # Fallback Xiaomi
    "deepseek-ai/DeepSeek-V3.2": "deepseek-ai/DeepSeek-V3", # Fallback V3
    "meta-llama/Llama-4-Scout-17B-16E-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct", # Fallback Llama 3.1
    "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16": "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", # Fallback Nemotron
    "default": "meta-llama/Meta-Llama-3-8B-Instruct"
}

# URL de base du routeur d'inférence HF
HF_INFERENCE_BASE = "https://router.huggingface.co/hf-inference/models"

# --- SCHEMAS ---
class ChatRequest(BaseModel):
    question: str
    model: Optional[str] = "default"
    system_prompt: Optional[str] = None
    temperature: Optional[float] = 0.7
    max_tokens: Optional[int] = 1024

class ChatResponse(BaseModel):
    answer: str
    model_used: str
    sources: list = []

# --- SECURITE ---
async def verify_api_key(authorization: str = Header(None)):
    if not authorization:
        raise HTTPException(status_code=401, detail="Missing Authorization header")
    
    try:
        scheme, token = authorization.split()
        if scheme.lower() != 'bearer':
            raise HTTPException(status_code=401, detail="Invalid authentication scheme")
        
        if token != CYGNIS_API_KEY:
            raise HTTPException(status_code=403, detail="Invalid API Key")
            
    except ValueError:
        raise HTTPException(status_code=401, detail="Invalid authorization header format")

# --- ENDPOINTS ---

@app.get("/")
def read_root():
    return {"status": "online", "service": "CygnisAI Studio API"}

@app.post("/api/ask", response_model=ChatResponse)
async def ask_model(req: ChatRequest, authorized: bool = Depends(verify_api_key)):
    if not HF_TOKEN:
        print("⚠️ WARNING: HF_TOKEN not set. Calls to HF will fail.")
    
    # 1. Sélection du modèle
    model_id = MODELS.get(req.model, MODELS["default"])
    api_url = f"{HF_INFERENCE_BASE}/{model_id}"
    
    print(f"🤖 Routing request to: {model_id}")

    # 2. Construction du prompt
    # On utilise le format standard chat template si possible, sinon raw text
    messages = []
    if req.system_prompt:
        messages.append({"role": "system", "content": req.system_prompt})
    messages.append({"role": "user", "content": req.question})

    payload = {
        "model": model_id,
        "messages": messages,
        "max_tokens": req.max_tokens,
        "temperature": req.temperature,
        "stream": False
    }

    headers = {
        "Authorization": f"Bearer {HF_TOKEN}",
        "Content-Type": "application/json"
    }

    try:
        # 3. Appel à Hugging Face (Endpoint compatible OpenAI)
        # Note: router.huggingface.co supporte souvent /v1/chat/completions
        # Si ça échoue, on tentera l'appel direct
        hf_chat_url = f"{HF_INFERENCE_BASE}/{model_id}/v1/chat/completions"
        
        response = requests.post(hf_chat_url, headers=headers, json=payload)
        
        # Fallback si le endpoint OpenAI n'est pas supporté pour ce modèle
        if response.status_code == 404:
             print("🔄 Fallback to standard inference API")
             # Pour l'API standard, on doit souvent envoyer une string unique
             # Ceci est une simplification, idéalement on utiliserait le tokenizer du modèle
             prompt_str = f"System: {req.system_prompt}\nUser: {req.question}\nAssistant:" if req.system_prompt else f"User: {req.question}\nAssistant:"
             
             payload_standard = {
                 "inputs": prompt_str,
                 "parameters": {
                     "max_new_tokens": req.max_tokens,
                     "temperature": req.temperature,
                     "return_full_text": False
                 }
             }
             response = requests.post(api_url, headers=headers, json=payload_standard)

        if response.status_code != 200:
            print(f"❌ HF Error ({response.status_code}): {response.text}")
            raise HTTPException(status_code=502, detail=f"Model provider error: {response.text}")

        data = response.json()
        
        # Parsing de la réponse (gère les deux formats possibles)
        answer = ""
        if "choices" in data and len(data["choices"]) > 0:
            answer = data["choices"][0]["message"]["content"]
        elif isinstance(data, list) and len(data) > 0 and "generated_text" in data[0]:
            answer = data[0]["generated_text"]
        elif "generated_text" in data:
            answer = data["generated_text"]
        else:
            answer = "Error: Could not parse model response."

        return {
            "answer": answer,
            "model_used": model_id,
            "sources": []
        }

    except Exception as e:
        print(f"❌ Internal Error: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)